Pengelompokan Wilayah Rawan Bencana Banjir di Kalimantan Barat Menggunakan Algoritma DBSCAN
DOI:
https://doi.org/10.55606/juitik.v6i2.2191Keywords:
Clustering, Data Mining, DBSCAN, Floods, Silhouette CoefficientAbstract
Floods represent an environmental response to natural dynamics and ecological degradation accelerated by anthropogenic activities. West Kalimantan is one of the regions with a high level of vulnerability to flood disasters, particularly in several regencies with distinct geoclimatic characteristics. This study aims to cluster flood-prone areas using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, based on climate data and flood occurrences from 2019 to 2023. Secondary data were obtained from the Regional Disaster Management Agency (BPBD) and the Meteorology, Climatology, and Geophysics Agency (BMKG), comprising variables such as average temperature, air humidity, rainfall, wind direction at maximum speed, and duration of sunlight. The study's findings indicate that the optimal configuration of DBSCAN parameters (ε = 0.25; MinPts = 5) resulted in five regional clusters, with an Average Silhouette Coefficient value of 0.795, indicating a strong clustering structure. Spatial visualization using Google Data Studio revealed that Ketapang, Melawi, Sintang, Kapuas Hulu, and Sambas are regions with significant flood risk. These findings demonstrate the effectiveness of DBSCAN in supporting data-driven disaster risk mapping.
References
Aggarwal, A., Bagri, N., Chandra, R., Thakur, M. B., Rani, A., & Surana, A. et al. (2022). Diagnostic accuracy of CT Hounsfield unit in distinguishing exudative and transudative pleural effusion. Journal of Medical Sciences and Health, 8(1), 14-21. https://doi.org/10.46347/jmsh.2022.v8i1.4
Aini, A. F., & Noveyani, A. E. (2023). Geographical distribution of pleural effusion among hospitalized patients in Jember Pulmonary Hospital. Caring: Indonesian Journal of Nursing Science, 5(2), 79-86. https://doi.org/10.32734/ijns.v5i2.13641
Çullu, N., Kalemci, S., Karakaş, Ö., Eser, I., Yalçin, F., & Boyaci, F. N. et al. (2014). Efficacy of CT in diagnosis of transudates and exudates in patients with pleural effusion. Diagnostic and Interventional Radiology, 20(2), 116-120. https://doi.org/10.5152/dir.2013.13066
Dewi, H., & Fairuz, F. (2020). Karakteristik pasien efusi pleura di Kota Jambi. Jambi Medical Journal (Jurnal Kedokteran dan Kesehatan Universitas Jambi, 7(2), 45-51. https://doi.org/10.22437/jmj.v8i1.9489
Durmaz, F., Özgökçe, M., Ayyıldız, V. A., Çilingir, B. M., & Göya, C. (2020). Can computerized tomography Hounsfield unit values be useful in the differential diagnosis of pleural effusion? Journal of Research in Clinical Medicine, 8(1), 26. https://doi.org/10.34172/jrcm.2020.026
He, T., & Oh, S. (2018). Diagnostic approach to pleural effusions. AME Medical Journal, 3, 116. https://doi.org/10.21037/amj.2018.12.02
Hussein, M., Elshabrawy, A., & Abdelrahman, A. (2024). Etiology of exudative pleural effusion among adults: Differentiating between tuberculous and other causes-a multicenter prospective cohort study. Egyptian Journal of Bronchology, 18, 12. https://doi.org/10.1183/13993003.congress-2023.PA5086
Jany, B., & Welte, T. (2019). Pleural effusion in adults-etiology, diagnosis, and treatment. Deutsches Ärzteblatt International, 116, 377-386. https://doi.org/10.3238/arztebl.2019.0377
Kocijancic, I., Vidmar, K., & Ivanovic, S. (2004). The value of CT attenuation of pleural fluid in differentiating exudative from transudative pleural effusions. European Radiology, 14(3), 497-502.
Light, R. W. (2002). Pleural effusion. New England Journal of Medicine, 346(25), 1971-1977. https://doi.org/10.1056/NEJMcp010731
Maheshwari, R., Naik, U., & Raghuraj, U. (2021). Association of CT Hounsfield unit value of pleural effusion with pleural fluid analysis. International Journal of Radiology Sciences, 3(1), 17-21. https://doi.org/10.33545/26649810.2021.v3.i1a.12
Mandal, A., Ghosh, S., & Das, S. K. et al. (2023). Etiological profile of pleural effusion in adults: A hospital-based study. Journal of the Association of Physicians of India, 71(5), 11-15.
Mercer, R. M., Corcoran, J. P., Porcel, J. M., Rahman, N. M., & Psallidas, I. (2019). Interpreting pleural fluid results. Clinical Medicine, 19(3), 213-217. https://doi.org/10.7861/clinmedicine.19-3-213
Nandalur, K. R., Hardie, A. H., & Bollampally, S. R. et al. (2005). Accuracy of computed tomography attenuation values in the characterization of pleural fluid: An ROC study. Academic Radiology, 12(8), 987-991. https://doi.org/10.1016/j.acra.2005.05.002
Porcel, J. M. (2011). Pearls and myths in pleural fluid analysis. Respirology, 16(1), 44-52. https://doi.org/10.1111/j.1440-1843.2010.01794.x
Porcel, J. M. (2023). Advances in the diagnosis of pleural effusion. The Lancet Respiratory Medicine, 11(6), 557-570.
Porcel, J. M., & Pardina, M. (2021). Imaging of pleural disease. Clinics in Chest Medicine, 42(2), 223-238. https://doi.org/10.1016/S0272-5231(21)01208-9
Vorster, M. J., Allwood, B. W., Diacon, A. H., & Koegelenberg, C. F. N. (2015). Tuberculous pleural effusions: Advances and controversies. Journal of Thoracic Disease, 7(6), 981-991.
Wahyuni, S., Handayani, D., & Pratama, R. (2018). Karakteristik klinis dan faktor sosial pasien efusi pleura di rumah sakit rujukan. Jurnal Respirasi Indonesia, 38(4), 210-216.
World Health Organization. (2013). Health literacy and health outcomes. Geneva: World Health Organization.
Wulandari, S., Syahrani, F., Rahmaini, A., & Eyanoer, P. C. (2021). Pleural fluid leukocyte level test for establishing tuberculous pleural effusion. Official Journal of the Indonesian Society of Respirology, 41(3), 156-160. https://doi.org/10.36497/jri.v41i3.182
Yamada, A. (2024). Imaging of pleural disease: CT features and diagnostic approach. Radiographics, 44(2), e230079. https://doi.org/10.1148/rg.230079
Yilmaz, U., Polat, G., & Sahin, N. et al. (2010). CT attenuation values of pleural effusions in differentiation of transudates and exudates. Clinical Imaging, 34(4), 270-274.
Zhang, Y., Zhang, Y., Wang, W., Feng, X., Guo, J., & Chen, B. et al. (2024). Diagnostic accuracy of thoracic CT to differentiate transudative from exudative pleural effusion prior to thoracentesis. Respiratory Research, 25(1). https://doi.org/10.1186/s12931-024-02745-3
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